Text Ranking
sentence-transformers
Safetensors
Transformers
Arabic
xlm-roberta
text-classification
text-embeddings-inference
Instructions to use Omartificial-Intelligence-Space/ARA-Reranker-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Omartificial-Intelligence-Space/ARA-Reranker-V1 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Omartificial-Intelligence-Space/ARA-Reranker-V1") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Transformers
How to use Omartificial-Intelligence-Space/ARA-Reranker-V1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Omartificial-Intelligence-Space/ARA-Reranker-V1") model = AutoModelForSequenceClassification.from_pretrained("Omartificial-Intelligence-Space/ARA-Reranker-V1") - Notebooks
- Google Colab
- Kaggle
Create readme.md
Browse files
README.md
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---
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license: apache-2.0
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language:
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- ar
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pipeline_tag: text-classification
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tags:
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- transformers
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- sentence-transformers
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- text-embeddings-inference
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---
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# Introducing ARM-V1 | Arabic Reranker Model (Version 1)
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**For more info please refer to this blog: [ARM | Arabic Reranker Model](www.omarai.me).**
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✨ This model is designed specifically for Arabic language reranking tasks, optimized to handle queries and passages with precision.
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✨ Unlike embedding models, which generate vector representations, this reranker directly evaluates the similarity between a question and a document, outputting a relevance score.
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✨ Trained on a combination of positive and hard negative query-passage pairs, it excels in identifying the most relevant results.
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✨ The output score can be transformed into a [0, 1] range using a sigmoid function, providing a clear and interpretable measure of relevance.
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